Modeling Phase Transitions in Gene Expression State Space

描述（由申请人提供）：生物学中成功的定量方法包括建立详细的局部模型或在高通量数据中检测鲁棒信号。在这项提议中，这两种方法结合在一起，以一种创新的方式来研究致癌病毒感染后人体组织中的转录变化。这类病毒可以造成一系列后果，从细胞表型的微小变化到剧烈转变。从包含所有已知病毒-宿主相互作用信息的种子网络开始，将在基因表达数据上学习贝叶斯转录网络。然后将贝叶斯网络转换为相互作用的电磁自旋的等效系统。这种自旋系统的例子已经在统计物理中研究过，并且已知它们具有丰富的相结构。与宿主细胞网络相对应的自旋系统将被模拟，排列自旋的域将被识别为表征细胞对扰动反应的遗传模块。这些模块的激活水平将用于在基因表达状态空间中划分阶段。以这种方式发现的新相和相变将通过实验进行验证。该框架从有噪声的高吞吐量数据中筛选出概率相互作用，然后根据得到的网络模型做出新的预测。这是一种新的、定量的、生物信息性的方法来模拟对人类细胞的扰动。在临床层面上，它可以用来精确区分病人的各种正常和疾病状态，并计算出哪种治疗方法能最好地逆转疾病的进展。这项技术有可能使医疗诊断和治疗更有效、更有针对性和更精确。

DESCRIPTION (provided by applicant): Successful quantitative approaches in biology have included building detailed local models or detecting robust signals in high-throughput data. In this proposal, both these methods are combined in an innovative way to study transcriptional changes in human tissue upon infection by oncogenic viruses. Such viruses can have a range of consequences, from minor changes to drastic transformations in the cell phenotype. Starting from a seed network consisting of all known information about the viral-host interaction, a Bayesian transcriptional network will be learned on the gene expression data. The Bayesian network is then transformed into an equivalent system of interacting electromagnetic spins. Examples of such spin systems have been studied in statistical physics, and they are known to have rich phase structures. The spin system corresponding to the host cell network will be simulated, and domains of aligned spins will be identified as genetic modules that characterize the response of the cell to perturbations. The activation levels of these modules will be used to demarcate phases in the gene expression state space. Novel phases and phase transitions discovered in this way will then be validated by experiments. This framework sifts out probabilistic interactions from noisy high- throughput data and then makes novel predictions based on the resulting network model. It is a new, quantitative, and biologically informative way to model perturbations to human cells. On a clinical level, it could be used to finely differentiate between various normal and disease states in patients, and to calculate which therapies would best reverse the progression of a disease. This technique has the potential to make medical diagnosis and treatment more efficient, directed and precise. PUBLIC HEALTH RELEVANCE: The goal of my research project is to quantify how perturbations to the human transcriptional network cause transitions between different phenotypes. Working in this framework, clinicians will be able to detect disease states using widely available high-throughput methods. They can then determine the personalized treatment, or combination of treatments, that will most efficiently reverse disease progression in a particular patient.